Over the last year, I’ve talked to dozens of Oracle Analytics and AI leaders. Everyone is excited about how Artificial Intelligence will transform how decisions are made and the resulting actions people take.
In all the discussion, 5 key questions came up again and again so I’m sharing them here, along with perspectives from the Oracle Analytics community.
1. Are we beyond dashboards?
This was a resounding yes, although for some it was more aspirational. The consensus, though, was that conversational analytics is replacing traditional dashboards. Business executives are frustrated by dashboard proliferation—instead of searching spreadsheets, now they’re searching canvases and visualizations for answers to pressing questions. They’re already accustomed to a conversational style in Internet searches and expect the same from analytics.
Alan Petrie, Head of Corporate Data and Analytics at Heathrow Airport, “When someone has a business problem to solve, it can be time consuming to have to go and search through dashboards to find the specific visualization to provide answers. They must know where to look. With AI and conversational interfaces, users should be able to say or type ‘show me the average salaries of my department over the past three years’ and have the correct visualization created for them on the fly.”
Bharat Kumar, Senior Finance Systems Lead at Zoom, “Relying exclusively on dashboards and reporting for information is becoming obsolete. AI can empower users to ask any question using natural language and automatically create the views on our curated data to provide an answer.”
Todd Randolph, Principal, Advisory at KPMG, “The request will no longer be ‘I need a new report’, to be created by an analytics specialist, but ‘tell me what I need to know.’”
Giorgio Ticinelli, BI Leader in Corporate Systems at dunnhumby, “There is a thirst in the user community to be able to augment dashboards with conversational interfaces, enabling them to ask any question of their data on the fly.”
We’ve talked for a long time about the value of democratizing analytics, and making insights easily accessible via AI and conversational interfaces will do just that. Access to relevant information will be much faster, and it will be much easier for analytics to be a seamless part of any executive’s workflow.
2. How can AI enable us to move from reactive to proactive?
Since its inception, analytics has followed the same paradigm: “ask a question, get an answer.” No longer—business leaders now expect to be prompted when something relevant happens. “Don’t make me ask, tell me what I need to know” for proactive anomaly detection, predicting an event or outcome, making a recommendation, creating an alert, highlighting an emerging trend, and much more.
Carmen Rooksberry, Assistant Vice President for Business Applications at Nemours Children’s Health, “I want the platform to do the analysis for users, drawing things to their attention. For an example from Accounts Payable: ‘Hey, I noticed this bill was paid within the last 60 days for last four years, but it didn’t get paid this year. You might want to look at that.’”
Colm McMahon, Research Director at University College Dublin Centre for Clinical Research, “AI is particularly useful in exploring data to find patterns, spot trends or anomalies, and enable clinicians to use natural language. For example, they can say ‘Tell me what’s going on in this data that might help me improve my patient’s care’ and get a response that is particular to the specific patient’s history, triggers, and needs.”
Analytics and AI leaders look forward to a point in the not-so-distant future when Agentic AI will enable executives to have an ‘always-on’ business analyst that continuously scans data and automatically surfaces risks or opportunities.
3. How will AI agents empower human agents?
AI Agents will also be integrated into workflows, acting on behalf of users. Decisions can be automated—in some cases without needing human intervention—coordinating and orchestrating workflows.
Alan Petrie, Head of Corporate Data and AI at Heathrow, “We’ll have an AI concierge who takes the request, and passes it off to whichever agents are needed, potentially multiple agents that can talk to each other. Essentially, it’s like creating a new role in the company.”
Arun Kumar, Global Lead – Supply Chain Excellence at MTN, “We see particular value in Oracle’s embedded AI agents which will allow business users to interact with data in natural language, surface anomalies automatically, and receive prescriptive recommendations directly in the flow of work.”
We see customers actively preparing to have AI Agents to assist humans making decisions, and in some cases preparing to automate decision making completely.
4. What do we have to do to make AI successful?
Again, comments here were resounding and unanimous: unified, trusted data foundations are essential for AI success.
Customers and partners alike emphasised the importance of preparing data sources and pipelines. They also talked about the importance of ensuring that governance and guardrails are in place to protect individuals and organisations alike. If anything, AI makes it more complex to move solutions from concept to production, so focusing on the fundamentals is essential for successful deployment of AI initiatives at scale.
Myles Gilsenan, Vice President of Data, Analytics and AI at Apps Associates, “When your boss says to you, ‘It’s time to get serious about AI’, that means AI projects need to move past the POC stage and become full-fledged production AI applications. So, what’s the first thing you must do? Get control of your data. Not just databases from corporate systems of record, but ALL data—structured and unstructured: sales contracts, purchase contracts, policies, procedures, multimedia recordings, and images—should contribute to AI and machine learning applications.”
Arun Kumar, MTN, “By consolidating Oracle Fusion Cloud Applications data with external operational, network, and logistics data into a single governed model, we expect to accelerate use cases such as predictive risk scoring, AI-assisted planning, and near real-time working capital optimization across all major markets.”
Tony Cassidy, CEO and founder at Vertice, “To get value at an enterprise level, you need to deal with the data optimally—lots of data for enrichment and value, and the pipelines that deliver that data. With Oracle’s new AI Data Platform, we have seen technology mature to enable customers to take advantage of these very large data sets for good time to value. You still need solution engineers to take care of the data pipelines, and a team with excellent data analytics knowledge. When organizations have that, when they’re able to control all the innovations and mechanisms for managing data, they have a great chance at delivering AI successfully and at scale.”
5. Does AI replace self-service analytics?
We weren’t sure how this would go. It turned out that organizations see AI as empowering their people, helping get analytics capabilities distributed broadly across the enterprise. There is a desire to lower adoption barriers by empowering business users and reducing their dependence on technical specialists.
Todd Randolph, KPMG, “In the past, our tendency was to build dashboards with charts for the metrics we believed business users needed to manage their business. With AI, that has evolved. Prompts powered by AI, natural language queries, and conversational interfaces allow users to explore the data to find out what’s really happened, and what to do next.”
Jommi Pätäri leads the finance data and analytic teams at the City of Espoo, “We think AI will help us with natural language querying. It can give information directly to executives on demand. AI will become our digital partner—someone you can ask for insights and analysis on the fly.”
AI is accelerating the democratization of enterprise analytics by lowering technical barriers to insight generation.
AI changes everything…except the need for good data
It’s clear from Analytics leaders’ comments that they see enterprise analytics evolving from static reporting into AI-driven conversational decision intelligence, with AI agents delivering proactive insight generation, recommending action, and embedding decision support directly in operational workflows.
At the same time, AI enables organizations to handle more data, from more diverse sources, and much of it is unstructured. Managing all this data professionally remains paramount and is at the heart of successful deployment of AI at enterprise scale.
Learn More
To learn more from Analytics leaders, visit the Oracle Analytics Blog, or join the Oracle Analytics Community where you can ask them questions and follow their insights.